Strengths: The strength of a large and established enterprise is its current business. Your organisation has built up its business over a long time. You have an established customer base, established processes, economies of scale and brand equity. If you are a bank, then you probably have a competitive advantage in cost of funds.

Weaknesses: To work properly, data science must be integrated into your organisation. You will need new processes, new teams, new specialised roles, new ways of working, new infrastructure. The data science architecture as presented in the previous article, is radically different to what most large organisations currently have implemented. For example, many ASX50 companies do not have real time personalisation on their websites. Change takes time in large organisations. The right companies are building their data science platforms right now.

Information asymmetry. Data science is a new field and everyone is a self proclaimed expert. Senior leaders who have come up through the business side need to sift through the salespeople – despite the information asymmetry. Which consultants to engage? Whom to hire? Whom to promote? Whose experience is relevant?

Opportunities: Properly productionised data science can increase the profitability of any large enterprise. Although some companies are further ahead than others, everyone is just beginning their journey. This is the opportunity to pull ahead of your competitors – if you get it right. Employees at all levels have the opportunity to grow their careers by building up a track record with the right experience.

Threats: If your competitors go further along the data science journey by a meaningful amount, then you will lose market share. Properly productionised data science can be an unfair advantage against an unprepared competitor. For example, your competitor could send a just-in-time retention offer before you have even noticed that you have acquired a new customer.

Career threats for technical staff. Working on successful, cutting edge projects is career gold. Working with stale technologies and irrelevant KPIs is career death because you will be de-skilling yourself. Team members who understand this will leave to work on cutting edge projects. On the other hand, team members who work on cutting edge projects will also leave when they find higher paying jobs.

Career threats for management staff. Building a track record as a leader in a leading company is a great career boost. However, if you recruit the wrong team, the implementation of data science solutions in your company may fall in the wrong direction.

The Enterprise Data Science Architecture Conference focuses on how to properly productionise data science solutions at scale. We have confirmed speakers from ANZ Bank, Coles Group, SEEK, ENGIE, Latitude Financial, Microsoft, AWS and Growing Data. The combination of presentations is intended to paint a complete picture of what it takes to productionise a profitable data science solution. As an industry, we are figuring out how to best build end-to-end machine learning solutions. As the field matures, knowledge of best practices in end-to-end machine learning pipelines will become essential skills. I invite you to view our list of confirmed speakers and talks at https://edsaconf.io because this is the right place to meet the right people and up-skill.

Meet the right people and up-skill. The conference will be on the 27th March at the Melbourne Marriott Hotel. A fully catered conference with coffee, lunch, morning/afternoon tea and evening drinks & canapes. I invite you to reserve your place at https://edsaconf.io this is the best place to learn the emerging best practices.

Slava Razbash, has worked in data science roles in multinational enterprises,startups and even a university. He has a solid track record that includes working in CBA’s big data team and helping start Sportsbet’s datascience and personalisation capability. Slava is the Founder of the Enterprise Data Science Architecture Conference.

Reserve your place today at https://edsaconf.io because you must keep your skills current.